We are interested in whether recommendation lists contain a signal of user attributes and whether this signal is strengthened when the user attribute is explicitly part of the training data.
Previous work studying a user signal in recommender output is limited.
In [1]}, the output of a recommender system is combined with a limited number of known transactions to infer unknown transactions of a target user.
Our work is closer to [2]}, [3]}, which focus on user attributes, specifically, infer gender, age, and occupation of target users based on recommendation lists for those users combined with additional information.
In [2]}, the additional information is user embeddings that represent users internal to the recommender system.
In [3]}, the additional information is the user's original profile, which is also internal to the recommender system.
To our knowledge, we are the first to carry out user attribute inference only on the recommendations that were produced by the system without adding internal information.
